Towards Concept-Based Translation Models Using Search Logs for Query Expansion

  • Authors:

  • Jianfeng Gao;Jian-Yun Nie

  • Affiliations:

  • Microsoft Research One Microsoft Way Redmond, WA 98052, USA;University of Montreal CP. 6128, succursale Centre-ville Montreal, Quebec H3C 3J7, Canada

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

Query logs have been successfully used to improve Web search. One of the directions exploits user clickthrough data to extract related terms to a query to perform query expansion (QE). How-ever, term relations have been created between isolated terms without considering their context, giving rise to the problem of term ambiguity. To solve this problem, we propose several ways to place terms in their contexts. On the one hand, contiguous terms can form a phrase; and on the other hand, terms at proximi-ty can provide less strict but useful contextual constraints mutual-ly. Relations extracted between such more constrained groups of terms are expected to be less noisy than those between single terms. In this paper, the constrained groups of terms are called concepts. We exploit user query logs to build statistical translation models between concepts, which are then used for QE. We perform experiments on the Web search task using a real world data set. Results show that the concept-based statistical translation model trained on clickthrough data outperforms signif-icantly other state-of-the-art QE systems.